In all likelihood at least one of the members of the Colts football team is a heretic. The inexplicable turnaround in what was an amazing football team leaves me frustrated. I mean, there seems to be no basis for the exceptional favoring of the Patriots short of divine intervention. In fact, the whole ordeal reminds me very much of Homer’s “The Odyssey.” I am left without doubt that the Colts have their own Odysseus who was so self-confident that he decried God. And, began suffering his ordeal today. It is the only somewhat logical reason that I can think of for these evens. Though, I admit that I may have used more inductive reasoning than deductive. Can you come up with a better explanation?

Models and Concepts of Life and Intelligence

This chapter begins with a section that examines the theories regarding the mechanics of life, and thought. They begin by talking about how people have historically how we define things that are alive, or not. And, how people have always considered themselves to be both made of living matter, and continuous with inanimate mater. Then, they trying to establish a working definition for what is required for an entity to be alive, and man’s reluctance to accept things they have created to be alive. Ultimately, what they allude to is adaptation.

Next, they begin to example the nature of what it really means to be random. Much of the foundations of self organized systems rely on stochastic adaptation, so they try to determine if anything is really every “random.” They go through multiple examples of various events that we consider to be random, such and computer random number generators. For these types of events, that we know are deterministic, they label as “quasirandom” events. However, more complex events where we cannot observe all of the variables that result in an outcome are, random. Ultimately, what they decided is that random only means “unexpected outcome,” And that nothing truly happens without cause.

The following section examines what Gregory Bateson coined the “two great stochastic systems,” which are evolution, and mind. The section works through the interconnections between evolution, and the mind. Particularly, trying to explain a method of thought based on evolution. So called, “memes” that act like meta-physical genes and behavior in a similar manner. They do make a distinction between the two, stating the evolution removes the less fit from members of the population, while the mind adapts by changing the states of persisting members.

The Game of Life is then examined, as it illustrates a simple form of emergence. The Game of Life is a “game” that is setup on a grid; each cell in a grid has a certain set of rules that dictated its behavior based on the cells around it. A cell can be either “alive” or “dead.” They then try to deal with the slippery issue of what exactly emergence means. They talk about how complex behavior “emerges” from a series of relatively simple systems. Emergence is generally considered a characteristic of complex, or dynamic systems.

Cellular Automata (CA) provided the foundation for the Game of Life mentioned in the last section. Most cellular automata are one dimensional and binary. The book illustrates a simple example where by a center number is affected by its neighbors depending on its current state. This is a seemingly simple situation that can result in eight different outcomes. They discuss the different types of cellular automata: evolution leads to homogeneous state, a simple stable state or periodic structures, chaotic patterns, or complex localized structures. The fourth structure is the one of the most interest. It has been theorized that it can be manipulated in such a way to perform any kind of computation.

The following section began to examine artificial life as it develops within computer programs. They make the assertion that something need not behave like any “real” life to be living. In fact they may follow a set of characteristics completely like anything we have seen on Earth. They use CA’s as their “breeding stock” in a few examples. They introduce “random” mutations by flipping bits in the rule table. The change in the rules, results in a change in the system. This is likened to the difference between genotype, and phenotype. They then go on to multiple examples such as biomorphs, and Sims’ “seed” creatures.

The final section in this chapter examines intelligences, first in people then in machines. Much of what is considered to be human intelligence is based on the premise established by a psychologist named Boring. His idea is that human intelligence is whatever an intelligence test measures. This is actually an ironic situation, considering the current computers can be setup to easily complete current IQ tests with near, or perfect accuracy. Turing created the test to determine computer intelligence. In order for a computer to be intelligent, it has to fool a human into thinking it is communicating with a human. David Fogel contests that intelligence is something that should be measured equally between humans and computers, he defines it as the “ability of a system to adapt its behavior to meet its goals in a range of environments.”

By Steven Johnson

This book serves as a decent introduction into to self-organizing systems. He uses a broad range of examples that range from ants to video games. Much of the text is heavily researched, such as Resnick’s slime mold simulation, Gordon’s studies on ants, and many more, even reaching back as far as Turing in the twilight of his career. The bibliography itself makes up a substantial chunk of the book. However, he does have the tendency to make assumptions, and allows his personal bias to be shown. Many times to a fault, as they don’t seem to based on adequate research.

He seemed to focus on four key areas when discussing self-organizing systems: neighborhood interaction, pattern recognition, feedback, and indirect control. Within each section he used a broad variety of examples to try to illustrate his point. Initially, it seems somewhat eclectic, but you get used to it as you go along.

The section on neighborhood interaction seemed to be the basis for self-organizing systems. Without individual elements reacting, and communicating with other elements, they would just be completely autonomous pieces. The interaction between the individuals is what forms the foundation for the systems.

He continued to explain about Pattern Recognition, the basically dealt with the ability of multi-agent self-organized systems to recognize patterns that are more difficult for top down centralized entities to recognize. He heavily focus on the way the human brain works to illustrate his point here.

He split feedback into two distinct sections: positive, and negative feedback. Positive feedback systems feed on themselves to propel themselves onward faster and faster. The key example here was the modern media. However, the counter example was negative feedback. When a system receives negative feedback it must make changes, and adapt appropriately. The major example he used here was Slashdot’s community feedback system.

The final section of the second part of the book dealt with indirect control. My understanding is that this dealt with the emergence of an appearance of centralized behavior illustrated by the multi-agent systems. He focused a lot on video games in this section. Particularly, the Sims, and the variations thereof.

The third, and final section of the book dealt with his speculations, and assessments of what ever meant. Unfortunately, the ideas expressed here do not really seem to be substantial enough to take at face value. It is fairly obvious that he is illustrating lines of thought we are insufficiently researched, and heavily biased by his opinions.

All things considered, it was not the greatest book of its kind that I have read. But, it certainly wasn’t the worst either. It does prove to provide a good background, and underlying conceptual framework into multi-agent, self organized systems. It is just laced with a few inaccuracies, and biases.